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Soft Computing Library (numpysc-lib)

A Python package that stores practical programs for Soft Computing & Neural Networks (Simple Neural Networks, Sigmoidal Functions, McCulloch-Pitts, Hebb Rule, Backpropagation, Fuzzy Sets, Perceptron) and prints their source code on demand, from Python or the terminal.

Install it once with pip, then view any practical code on any machine without copying files around.


Practicals Included

Practical Topic / Description
1A-Simple-Neural-Network Design a Simple Neural Network (SNN) with Ramp activation
1B-Sigmoidal-Functions Binary and Bipolar Sigmoidal Activation Functions
2A-McCulloch-Pitts Neural Network ANDNOT function using McCulloch-Pitts Neuron
2B-XOR function using McCulloch-Pitts Neural Network XOR function using McCulloch-Pitts Network
3-AND Hebb Rule Network AND function with Hebbian learning rule (bipolar inputs)
4-Back Propagation Problem Single-step forward & backward error propagation
5-Error Back Propagation Multi-epoch Error Backpropagation Training
6-Identity Operators Identity and Membership Operators in Python
7-Fuzzy Fuzzy Set Operations (Union, Intersection, Complement, Algebraic/Bounded Sum & Diff)
8-Perceptron Perceptron Learning Algorithm for Linear Classification

Step 1: Install

pip install numpysc-lib

To update to the latest version:

pip install --upgrade numpysc-lib

Requires Python 3.6 or newer.


Step 2: View the programs

Option A: From the terminal

Show all practicals:

numpysc
# or
numpysc-lib

Show a single practical (type the name or partial name):

numpysc "1A-Simple-Neural-Network"
numpysc "7-Fuzzy"
numpysc "8-Perceptron"

Option B: From Python

import numpysc_lib

numpysc_lib.show_codes()                           # Show all practicals
numpysc_lib.show_codes("7-Fuzzy")                  # Show specific practical
numpysc_lib.show_codes("1A-Simple-Neural-Network") # Show specific practical

Option C: In Google Colab or Jupyter

!pip install numpysc-lib
import numpysc_lib
numpysc_lib.show_codes("8-Perceptron")

Publishing a new version (for maintainers)

  1. Make any additions or updates in the numpysc_lib/ folder.
  2. Increment version in setup.py (e.g. 1.1.2).
  3. Clean old builds:
    rm -rf dist build *.egg-info
    
  4. Build:
    python3 -m build
    
  5. Check and Upload to PyPI:
    python3 -m twine upload dist/*
    

License

MIT. See LICENSE.

Metadata

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